Driving risk assessment based on naturalistic driving study and driver attitude questionnaire analysis. (September 2020)

• Record Type: Journal Article
• Authors: Wang, Jianqiang; Huang, Heye; Li, Yang; Zhou, Hanchu; Liu, Jinxin; Xu, Qing
• Is Part Of: Accident analysis and prevention. Volume 145(2020)
• Publication Date: 2020-09
• Subjects: Intelligent vehicles -- Driving risk coupling model -- Internal and external field -- Naturalistic driving study -- Driver attitude questionnaire; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105680
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Combines data from naturalistic driving study and driver attitude questionnaire. Evaluates driving risks of different influence factors in dynamic environments Establishes the internal and external field to describe the risk influence. Develops a driving risk coupling model to output dynamic risk assessment Abstract: Traffic accident statistics have shown the necessity of risk assessment when driving in the dynamic traffic environment. If the risk associated with different traffic elements ( i.e., road, environment and vehicles) could be evaluated accurately, potential accidents could be significantly avoided or mitigated. This paper proposes a driving risk assessment model that can quantitatively evaluate the driving risk associated with intelligent vehicles via the coupled analysis of different traffic elements. First, we present a concept of the internal field and external field for establishing the driving risk coupling model, through employing the internal field to define the risk range of driver's perspective and the external field to calculate the risk coefficients of those traffic elements. Then, the relative risk coefficients are computed by incorporating both naturalistic driving study (NDS) and driver attitude questionnaire (DAQ) using a multinomial logit model. Specifically, we perform a large-scale naturalistic driving study to investigate the objective driving risks. Typical driver behavior parameters, such as velocity, time headway, and acceleration, are analyzed. Besides, a self-reported survey of 364 drivers is conducted to subjectively evaluate the potential risks that drivers may face in various situations. Finally, validation of the model is conducted by comparing the accuracy with the typical risk assessment index, i.e., TTC and THW. Results demonstrate that the proposed approach is effective in evaluating the comprehensive driving risks by quantifying the influence factors of driving risks in dynamic environments.